In the development of current Digital TV (DTV) systems, it is essential to employ video format conversion units because of the variety of the video formats adopted in many different DTV standards worldwide. For example, the ATSC DTV standard system of North America adopted 1080×1920 interlaced video, 720×1280 progressive video, 720×480 interlaced and progressive video, etc. as its standard video formats for digital TV broadcasting.
Video format conversion operation is to convert an incoming video format to a specified output video format to properly present the video signal on a display device (e.g., monitor, FLCD, Plasma display) which has a fixed resolution. A proper video format conversion system is important as it can directly affect the visual quality of the video of a DTV Receiver. Fundamentally, video format conversion operation requires advanced algorithms for multi-rate system design, poly-phase filter design, and interlaced to progressive scanning rate conversion or simply deinterlacing, where deinterlacing represents an operation that doubles the vertical scanning rate of the interlaced video signal.
Historically, deinterlacing algorithms were developed to enhance the video quality of NTSC TV receivers by reducing the intrinsic annoying artifacts of the interlaced video signal such as a serrate line observed when there is motion between fields, line flickering, raster line visibility, and field flickering. This also applies to the DTV Receiver.
Elaborate deinterlacing algorithms utilizing motion detection or motion compensation allow doubling the vertical scanning rate of the interlaced video signal especially for stationary (motionless) objects in the video signal. Motion detection based deinterlacing operation can be used for analog and digital TV receivers.
A number of deinterlacing algorithms exist. Such deinterlacing algorithms can be categorized into two classes: 2-D (spatial) deinterlacing algorithms and 3-D (spatio-termporal) deinterlacing algorithms depending on the use of motion information embedded in consecutive interlaced video sequence. It is well-known that a 3-D deinterlacing algorithm based on motion detection provides more pleasing performance than a 2-D deinterlacing algorithm. The key point of a 3-D deinterlacing algorithm is precisely detecting motion in the interlaced video signals.
Existing methods disclose estimating a motion decision factor based on the frame difference signal and the sample correlation in vertical direction. These methods provide a way of reducing the visual artifacts that can arise from false motion detection by utilizing the sample correlation in vertical direction of the sampling point where the value is to be interpolated. However, such methods may not provide a true motion detection method when there are high frequency components in vertical direction. As a consequence, such methods do not increase the vertical resolution even when there is no real motion between fields. In other methods, the filtering result of the frame difference is compared with a constant value to determine the motion detection signal. However, in such methods, performance deteriorates when noise in the video sequence increases.